Automatic reconstruction method for large scene based on multi-site point cloud stitching

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 590-596 ◽  
Author(s):  
Haonan Xu ◽  
Lei Yu ◽  
Junyi Hou ◽  
Shumin Fei
Keyword(s):  
Point Cloud ◽  
Reconstruction Method

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

Structural mapping of rock walls imaged with a LiDAR mounted on an unmanned aircraft system

2019 ◽  
Vol 7 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Connor McAnuff ◽  
Claire Samson ◽  
Dave Melanson ◽  
Christopher Polowick ◽  
Erin Bethell
Keyword(s):  
Point Cloud ◽  
Ground Level ◽  
Unmanned Aircraft ◽  
Reconstruction Method ◽  
Angle Of Incidence ◽  
Fracture Density ◽  
Mining Operations ◽  
Structural Mapping ◽  
Geological Information ◽  
Aircraft System

Structural mapping of rock walls to determine fracture orientation provides critical geological information in support of mining operations. A helicopter-style UAS (rotor diameter 2 m; take-off mass 35 kg; payload mass 11 kg) instrumented with a high-resolution LiDAR imaged a 75 m long and 10–15 m high series of four adjacent rock walls at the Canadian Wollastonite mine. A point cloud with a density of 484 point/m2 acquired at an angle of incidence of ∼41.7° from a flight altitude of 41.7 m above ground level was selected for structural mapping. The point cloud was first meshed using the Poisson surface reconstruction method and then remeshed to achieve an even element size distribution. Visualization of the remeshed Poisson mesh using a 360° hue–saturation–lightness colour wheel highlighted areas of higher fracture density, whereas visualization using a 180° colour wheel accentuated sliver-like geological features. Two joint sets were identified at 156/82 and 241/86 (strike/dip in degrees). A total of 18 virtual strike measurements and 13 virtual dip measurements were within 10% of manual compass measurements. This study demonstrated that the task of structural mapping of large rock walls can be automated by processing 3D images acquired with a LiDAR mounted on a UAS.

scholarly journals 3D recognition based on ordered images reconstruction

2018 ◽  
Vol 232 ◽  
pp. 02045
Author(s):  
Ning Zhang ◽  
YongJia Zhao
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Scene Recognition ◽  
Reconstruction Method ◽  
Processing Stage ◽  
Local Descriptor ◽  
Image Frame ◽  
Online Processing ◽  
Offline Processing ◽  
Sequential Images

Nowadays, more and more applications require precise and quickly 3D recognition, such as augmented reality and robot navigation. In recent years, model-based methods can get accurate object or scene recognition, but it takes a lot of time to reconstruct the model. Therefore, we propose a fast 3D reconstruction method based on ordered images for robust and accurate 3D recognition. The proposed algorithm consists of two parts, the offline processing stage, and the online processing stage. First, in the offline processing stage, the sparse point cloud model of the scene or object is reconstructed based on the sequential images, optimized using the BA algorithm based on the local correlation frame, and then the local descriptor of the resulting model points is stored. Secondly, in the online processing stage, for each image frame of the camera video, a matching relationship between the stored point cloud and the 2D feature points on the image frame is established, based on which the pose of the camera can be solved accurately.

Research on the Parameter Conversion of Mannequin Curve

2014 ◽  
Vol 543-547 ◽  
pp. 2656-2659
Author(s):  
Bo Ren ◽  
Ji Xin Yang ◽  
Peng Wan ◽  
Xue Heng Tao ◽  
Xue Jun Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Measuring System ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Engineering Software ◽  
Parameter Conversion ◽  
Dimensional Reconstruction ◽  
Design Software

In order to realize the reverse design of human bodys curve, the curves parameter conversion and reconstruction based on non-contact measuring system are studied in the paper. Firstly, obtain the model of point cloud data by the non-contact measurement system, and then import the data into reverse the engineering software Geomagic. Second, process the point cloud data with the method of human characteristic curves and surfaces division, structure fitting surface, and get the three-dimensional reconstruction model of human bodys point cloud data. Lastly, import the model into the forward design software Solidworks with different methods and edit it. Then finish the parameter conversion from Geomagic to the forward design software. The reconstruction method has a good value in reverse design of the mold.

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